Overview

Brought to you by YData

Dataset statistics

Number of variables15
Number of observations100000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory11.4 MiB
Average record size in memory120.0 B

Variable types

Numeric12
DateTime1
Categorical2

Alerts

AOV is highly overall correlated with ROAS and 1 other fieldsHigh correlation
Ad_CPC is highly overall correlated with Ad_SpendHigh correlation
Ad_CTR is highly overall correlated with Ad_SpendHigh correlation
Ad_Spend is highly overall correlated with Ad_CPC and 2 other fieldsHigh correlation
Clicks is highly overall correlated with Conversion_RateHigh correlation
Conversion_Rate is highly overall correlated with Clicks and 1 other fieldsHigh correlation
Impressions is highly overall correlated with Conversion_RateHigh correlation
ROAS is highly overall correlated with AOV and 2 other fieldsHigh correlation
Revenue is highly overall correlated with AOV and 1 other fieldsHigh correlation
Discount_Applied has 1685 (1.7%) zerosZeros
Clicks has 1999 (2.0%) zerosZeros
Conversion_Rate has 3621 (3.6%) zerosZeros

Reproduction

Analysis started2024-12-08 22:29:29.751739
Analysis finished2024-12-08 22:30:01.453078
Duration31.7 seconds
Software versionydata-profiling vv4.10.0
Download configurationconfig.json

Variables

Product_ID
Real number (ℝ)

Distinct1000
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean499.51473
Minimum0
Maximum999
Zeros110
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2024-12-08T23:30:01.644406image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile50
Q1250
median500
Q3749
95-th percentile950
Maximum999
Range999
Interquartile range (IQR)499

Descriptive statistics

Standard deviation288.77872
Coefficient of variation (CV)0.57811853
Kurtosis-1.2002857
Mean499.51473
Median Absolute Deviation (MAD)250
Skewness-3.3552055 × 10-5
Sum49951473
Variance83393.151
MonotonicityNot monotonic
2024-12-08T23:30:01.881771image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
521 137
 
0.1%
130 135
 
0.1%
933 133
 
0.1%
829 132
 
0.1%
719 132
 
0.1%
835 129
 
0.1%
111 127
 
0.1%
276 127
 
0.1%
1 126
 
0.1%
606 126
 
0.1%
Other values (990) 98696
98.7%
ValueCountFrequency (%)
0 110
0.1%
1 126
0.1%
2 106
0.1%
3 89
0.1%
4 102
0.1%
5 101
0.1%
6 85
0.1%
7 92
0.1%
8 109
0.1%
9 86
0.1%
ValueCountFrequency (%)
999 101
0.1%
998 116
0.1%
997 101
0.1%
996 89
0.1%
995 105
0.1%
994 107
0.1%
993 95
0.1%
992 89
0.1%
991 87
0.1%
990 114
0.1%
Distinct366
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size781.4 KiB
Minimum2023-12-07 00:00:00
Maximum2024-12-06 00:00:00
2024-12-08T23:30:02.114666image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:30:02.382317image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Units_Sold
Real number (ℝ)

Distinct344
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.32858
Minimum51
Maximum716
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2024-12-08T23:30:02.693243image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum51
5-th percentile61
Q184
median127
Q3166
95-th percentile208
Maximum716
Range665
Interquartile range (IQR)82

Descriptive statistics

Standard deviation49.679873
Coefficient of variation (CV)0.38713023
Kurtosis4.4677607
Mean128.32858
Median Absolute Deviation (MAD)41
Skewness0.86305924
Sum12832858
Variance2468.0897
MonotonicityNot monotonic
2024-12-08T23:30:02.991016image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
72 1133
 
1.1%
68 1102
 
1.1%
79 1066
 
1.1%
70 1062
 
1.1%
165 1046
 
1.0%
62 1026
 
1.0%
80 1020
 
1.0%
77 1019
 
1.0%
64 1009
 
1.0%
75 989
 
1.0%
Other values (334) 89528
89.5%
ValueCountFrequency (%)
51 106
 
0.1%
52 158
 
0.2%
53 284
 
0.3%
54 327
0.3%
55 307
0.3%
56 517
0.5%
57 495
0.5%
58 744
0.7%
59 688
0.7%
60 700
0.7%
ValueCountFrequency (%)
716 1
 
< 0.1%
706 1
 
< 0.1%
702 1
 
< 0.1%
692 1
 
< 0.1%
677 1
 
< 0.1%
663 1
 
< 0.1%
659 1
 
< 0.1%
656 1
 
< 0.1%
639 4
< 0.1%
636 1
 
< 0.1%

Discount_Applied
Real number (ℝ)

ZEROS 

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1498715
Minimum0
Maximum0.3
Zeros1685
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2024-12-08T23:30:03.213078image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.01
Q10.07
median0.15
Q30.22
95-th percentile0.29
Maximum0.3
Range0.3
Interquartile range (IQR)0.15

Descriptive statistics

Standard deviation0.086691785
Coefficient of variation (CV)0.57844077
Kurtosis-1.1911827
Mean0.1498715
Median Absolute Deviation (MAD)0.07
Skewness-0.00016104881
Sum14987.15
Variance0.0075154656
MonotonicityNot monotonic
2024-12-08T23:30:03.474532image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0.04 3440
 
3.4%
0.15 3434
 
3.4%
0.23 3411
 
3.4%
0.17 3403
 
3.4%
0.03 3395
 
3.4%
0.1 3388
 
3.4%
0.29 3381
 
3.4%
0.18 3376
 
3.4%
0.19 3374
 
3.4%
0.27 3371
 
3.4%
Other values (21) 66027
66.0%
ValueCountFrequency (%)
0 1685
1.7%
0.01 3358
3.4%
0.02 3305
3.3%
0.03 3395
3.4%
0.04 3440
3.4%
0.05 3259
3.3%
0.06 3348
3.3%
0.07 3234
3.2%
0.08 3292
3.3%
0.09 3289
3.3%
ValueCountFrequency (%)
0.3 1683
1.7%
0.29 3381
3.4%
0.28 3226
3.2%
0.27 3371
3.4%
0.26 3286
3.3%
0.25 3211
3.2%
0.24 3368
3.4%
0.23 3411
3.4%
0.22 3337
3.3%
0.21 3302
3.3%

Revenue
Real number (ℝ)

HIGH CORRELATION 

Distinct51950
Distinct (%)51.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean553.11081
Minimum7.65
Maximum5704.18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2024-12-08T23:30:03.711920image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum7.65
5-th percentile58.18
Q1221.26
median428.155
Q3805.865
95-th percentile1394.8115
Maximum5704.18
Range5696.53
Interquartile range (IQR)584.605

Descriptive statistics

Standard deviation428.36629
Coefficient of variation (CV)0.77446741
Kurtosis2.5510045
Mean553.11081
Median Absolute Deviation (MAD)264.82
Skewness1.1713089
Sum55311081
Variance183497.68
MonotonicityNot monotonic
2024-12-08T23:30:03.957225image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1078.59 13
 
< 0.1%
96.16 12
 
< 0.1%
89.91 11
 
< 0.1%
173.61 11
 
< 0.1%
65.5 11
 
< 0.1%
492.92 10
 
< 0.1%
346.74 10
 
< 0.1%
331.66 10
 
< 0.1%
155.66 10
 
< 0.1%
298.08 10
 
< 0.1%
Other values (51940) 99892
99.9%
ValueCountFrequency (%)
7.65 1
 
< 0.1%
8.19 2
 
< 0.1%
8.3 3
< 0.1%
8.39 1
 
< 0.1%
8.4 1
 
< 0.1%
8.41 2
 
< 0.1%
8.52 5
< 0.1%
8.53 1
 
< 0.1%
8.63 1
 
< 0.1%
8.64 2
 
< 0.1%
ValueCountFrequency (%)
5704.18 1
< 0.1%
5433.46 1
< 0.1%
5397.12 1
< 0.1%
4980.53 1
< 0.1%
4875.95 1
< 0.1%
4864.31 1
< 0.1%
4629.08 1
< 0.1%
4574.36 1
< 0.1%
4571.41 1
< 0.1%
4559.46 1
< 0.1%

Clicks
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct50
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.53917
Minimum0
Maximum49
Zeros1999
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2024-12-08T23:30:04.220187image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q112
median25
Q337
95-th percentile47
Maximum49
Range49
Interquartile range (IQR)25

Descriptive statistics

Standard deviation14.412477
Coefficient of variation (CV)0.58732535
Kurtosis-1.2001615
Mean24.53917
Median Absolute Deviation (MAD)12
Skewness-0.0049981154
Sum2453917
Variance207.71948
MonotonicityNot monotonic
2024-12-08T23:30:04.477452image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37 2091
 
2.1%
42 2083
 
2.1%
39 2066
 
2.1%
36 2063
 
2.1%
17 2060
 
2.1%
11 2059
 
2.1%
27 2057
 
2.1%
21 2052
 
2.1%
33 2049
 
2.0%
46 2030
 
2.0%
Other values (40) 79390
79.4%
ValueCountFrequency (%)
0 1999
2.0%
1 1990
2.0%
2 1959
2.0%
3 1923
1.9%
4 1984
2.0%
5 1992
2.0%
6 2028
2.0%
7 2015
2.0%
8 2012
2.0%
9 2006
2.0%
ValueCountFrequency (%)
49 1980
2.0%
48 1958
2.0%
47 2016
2.0%
46 2030
2.0%
45 2026
2.0%
44 2001
2.0%
43 1939
1.9%
42 2083
2.1%
41 1900
1.9%
40 1944
1.9%

Impressions
Real number (ℝ)

HIGH CORRELATION 

Distinct490
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean254.53906
Minimum10
Maximum499
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2024-12-08T23:30:04.740501image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile34
Q1132
median254
Q3377
95-th percentile475
Maximum499
Range489
Interquartile range (IQR)245

Descriptive statistics

Standard deviation141.46661
Coefficient of variation (CV)0.55577563
Kurtosis-1.2011747
Mean254.53906
Median Absolute Deviation (MAD)122
Skewness8.50296 × 10-5
Sum25453906
Variance20012.801
MonotonicityNot monotonic
2024-12-08T23:30:04.982589image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 240
 
0.2%
410 239
 
0.2%
376 238
 
0.2%
412 237
 
0.2%
212 237
 
0.2%
447 237
 
0.2%
196 236
 
0.2%
44 236
 
0.2%
133 235
 
0.2%
156 235
 
0.2%
Other values (480) 97630
97.6%
ValueCountFrequency (%)
10 208
0.2%
11 175
0.2%
12 198
0.2%
13 201
0.2%
14 207
0.2%
15 207
0.2%
16 187
0.2%
17 184
0.2%
18 191
0.2%
19 209
0.2%
ValueCountFrequency (%)
499 209
0.2%
498 201
0.2%
497 224
0.2%
496 221
0.2%
495 183
0.2%
494 208
0.2%
493 214
0.2%
492 188
0.2%
491 201
0.2%
490 198
0.2%

Conversion_Rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct333
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1981378
Minimum0
Maximum4.9
Zeros3621
Zeros (%)3.6%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2024-12-08T23:30:05.215700image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.01
Q10.05
median0.1
Q30.19
95-th percentile0.74
Maximum4.9
Range4.9
Interquartile range (IQR)0.14

Descriptive statistics

Standard deviation0.36118743
Coefficient of variation (CV)1.8229103
Kurtosis37.730717
Mean0.1981378
Median Absolute Deviation (MAD)0.06
Skewness5.3076844
Sum19813.78
Variance0.13045636
MonotonicityNot monotonic
2024-12-08T23:30:05.463309image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.09 5196
 
5.2%
0.08 5145
 
5.1%
0.07 5135
 
5.1%
0.05 5121
 
5.1%
0.06 5095
 
5.1%
0.04 5081
 
5.1%
0.02 5062
 
5.1%
0.03 5035
 
5.0%
0.1 4959
 
5.0%
0.01 4901
 
4.9%
Other values (323) 49270
49.3%
ValueCountFrequency (%)
0 3621
3.6%
0.01 4901
4.9%
0.02 5062
5.1%
0.03 5035
5.0%
0.04 5081
5.1%
0.05 5121
5.1%
0.06 5095
5.1%
0.07 5135
5.1%
0.08 5145
5.1%
0.09 5196
5.2%
ValueCountFrequency (%)
4.9 1
 
< 0.1%
4.8 5
< 0.1%
4.7 4
< 0.1%
4.6 4
< 0.1%
4.5 5
< 0.1%
4.45 3
< 0.1%
4.4 1
 
< 0.1%
4.36 3
< 0.1%
4.3 1
 
< 0.1%
4.27 5
< 0.1%

Category
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.4 KiB
2
21533 
4
20764 
3
19419 
1
19278 
0
19006 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters100000
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row3
3rd row4
4th row1
5th row0

Common Values

ValueCountFrequency (%)
2 21533
21.5%
4 20764
20.8%
3 19419
19.4%
1 19278
19.3%
0 19006
19.0%

Length

2024-12-08T23:30:05.677804image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-08T23:30:05.885809image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
2 21533
21.5%
4 20764
20.8%
3 19419
19.4%
1 19278
19.3%
0 19006
19.0%

Most occurring characters

ValueCountFrequency (%)
2 21533
21.5%
4 20764
20.8%
3 19419
19.4%
1 19278
19.3%
0 19006
19.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 100000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 21533
21.5%
4 20764
20.8%
3 19419
19.4%
1 19278
19.3%
0 19006
19.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 100000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 21533
21.5%
4 20764
20.8%
3 19419
19.4%
1 19278
19.3%
0 19006
19.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 100000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 21533
21.5%
4 20764
20.8%
3 19419
19.4%
1 19278
19.3%
0 19006
19.0%

Region
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.4 KiB
0
33472 
1
33265 
2
33263 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters100000
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row1
5th row2

Common Values

ValueCountFrequency (%)
0 33472
33.5%
1 33265
33.3%
2 33263
33.3%

Length

2024-12-08T23:30:06.089082image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-08T23:30:06.251241image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 33472
33.5%
1 33265
33.3%
2 33263
33.3%

Most occurring characters

ValueCountFrequency (%)
0 33472
33.5%
1 33265
33.3%
2 33263
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 100000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 33472
33.5%
1 33265
33.3%
2 33263
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 100000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 33472
33.5%
1 33265
33.3%
2 33263
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 100000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 33472
33.5%
1 33265
33.3%
2 33263
33.3%

Ad_CTR
Real number (ℝ)

HIGH CORRELATION 

Distinct1901
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.10498773
Minimum0.01
Maximum0.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2024-12-08T23:30:06.674731image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.0196
Q10.0575
median0.1052
Q30.1523
95-th percentile0.1907
Maximum0.2
Range0.19
Interquartile range (IQR)0.0948

Descriptive statistics

Standard deviation0.054806269
Coefficient of variation (CV)0.52202548
Kurtosis-1.1941851
Mean0.10498773
Median Absolute Deviation (MAD)0.0474
Skewness0.0028388654
Sum10498.773
Variance0.0030037271
MonotonicityNot monotonic
2024-12-08T23:30:06.924028image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.08 81
 
0.1%
0.1264 74
 
0.1%
0.1267 74
 
0.1%
0.1646 73
 
0.1%
0.0793 73
 
0.1%
0.0155 73
 
0.1%
0.1219 73
 
0.1%
0.0603 73
 
0.1%
0.13 73
 
0.1%
0.0339 73
 
0.1%
Other values (1891) 99260
99.3%
ValueCountFrequency (%)
0.01 25
< 0.1%
0.0101 40
< 0.1%
0.0102 54
0.1%
0.0103 57
0.1%
0.0104 57
0.1%
0.0105 57
0.1%
0.0106 59
0.1%
0.0107 48
< 0.1%
0.0108 57
0.1%
0.0109 58
0.1%
ValueCountFrequency (%)
0.2 29
< 0.1%
0.1999 50
0.1%
0.1998 57
0.1%
0.1997 53
0.1%
0.1996 60
0.1%
0.1995 55
0.1%
0.1994 43
< 0.1%
0.1993 55
0.1%
0.1992 56
0.1%
0.1991 66
0.1%

Ad_CPC
Real number (ℝ)

HIGH CORRELATION 

Distinct191
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0512502
Minimum0.1
Maximum2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2024-12-08T23:30:07.258287image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.19
Q10.58
median1.05
Q31.53
95-th percentile1.91
Maximum2
Range1.9
Interquartile range (IQR)0.95

Descriptive statistics

Standard deviation0.54925209
Coefficient of variation (CV)0.52247514
Kurtosis-1.203856
Mean1.0512502
Median Absolute Deviation (MAD)0.48
Skewness-0.0025045966
Sum105125.02
Variance0.30167786
MonotonicityNot monotonic
2024-12-08T23:30:07.613049image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.66 580
 
0.6%
0.67 577
 
0.6%
1.97 575
 
0.6%
1.66 575
 
0.6%
0.76 571
 
0.6%
1.37 570
 
0.6%
1.96 568
 
0.6%
0.3 566
 
0.6%
1.69 566
 
0.6%
0.16 564
 
0.6%
Other values (181) 94288
94.3%
ValueCountFrequency (%)
0.1 268
0.3%
0.11 503
0.5%
0.12 507
0.5%
0.13 521
0.5%
0.14 553
0.6%
0.15 513
0.5%
0.16 564
0.6%
0.17 548
0.5%
0.18 516
0.5%
0.19 529
0.5%
ValueCountFrequency (%)
2 276
0.3%
1.99 522
0.5%
1.98 540
0.5%
1.97 575
0.6%
1.96 568
0.6%
1.95 495
0.5%
1.94 507
0.5%
1.93 494
0.5%
1.92 518
0.5%
1.91 546
0.5%

Ad_Spend
Real number (ℝ)

HIGH CORRELATION 

Distinct27756
Distinct (%)27.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean110.47724
Minimum1.05
Maximum399.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2024-12-08T23:30:07.858448image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1.05
5-th percentile11.68
Q138.61
median87.24
Q3164.22
95-th percentile286.9505
Maximum399.6
Range398.55
Interquartile range (IQR)125.61

Descriptive statistics

Standard deviation87.015846
Coefficient of variation (CV)0.78763595
Kurtosis0.040110794
Mean110.47724
Median Absolute Deviation (MAD)56.57
Skewness0.91088071
Sum11047724
Variance7571.7574
MonotonicityNot monotonic
2024-12-08T23:30:08.117988image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20.9 23
 
< 0.1%
26.4 22
 
< 0.1%
31.62 22
 
< 0.1%
57.72 22
 
< 0.1%
22.18 22
 
< 0.1%
14.76 22
 
< 0.1%
34.1 21
 
< 0.1%
31.46 21
 
< 0.1%
19.55 21
 
< 0.1%
27.72 21
 
< 0.1%
Other values (27746) 99783
99.8%
ValueCountFrequency (%)
1.05 1
< 0.1%
1.11 1
< 0.1%
1.12 1
< 0.1%
1.13 1
< 0.1%
1.16 1
< 0.1%
1.21 1
< 0.1%
1.22 1
< 0.1%
1.23 1
< 0.1%
1.24 1
< 0.1%
1.25 1
< 0.1%
ValueCountFrequency (%)
399.6 1
 
< 0.1%
397.8 1
 
< 0.1%
397.4 2
< 0.1%
396.41 1
 
< 0.1%
395.6 3
< 0.1%
395.21 2
< 0.1%
395.2 1
 
< 0.1%
394.42 1
 
< 0.1%
394 1
 
< 0.1%
393.82 1
 
< 0.1%

AOV
Real number (ℝ)

HIGH CORRELATION 

Distinct98494
Distinct (%)98.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.0124039
Minimum0.04076555
Maximum35.787407
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2024-12-08T23:30:08.390291image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.04076555
5-th percentile0.45048427
Q11.7163424
median3.6395381
Q36.8815433
95-th percentile14.503062
Maximum35.787407
Range35.746642
Interquartile range (IQR)5.1652008

Descriptive statistics

Standard deviation4.5725213
Coefficient of variation (CV)0.91224119
Kurtosis3.4663514
Mean5.0124039
Median Absolute Deviation (MAD)2.2828193
Skewness1.6956415
Sum501240.39
Variance20.907951
MonotonicityNot monotonic
2024-12-08T23:30:08.633992image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.2 4
 
< 0.1%
4.345 4
 
< 0.1%
5.55 4
 
< 0.1%
4.97 4
 
< 0.1%
4.18 4
 
< 0.1%
2.03 4
 
< 0.1%
2.44 4
 
< 0.1%
0.76 4
 
< 0.1%
0.47 4
 
< 0.1%
0.7614285714 4
 
< 0.1%
Other values (98484) 99960
> 99.9%
ValueCountFrequency (%)
0.04076555024 1
< 0.1%
0.04401960784 1
< 0.1%
0.04460784314 1
< 0.1%
0.04507936508 1
< 0.1%
0.04656565657 1
< 0.1%
0.04722222222 1
< 0.1%
0.04848039216 1
< 0.1%
0.04904255319 1
< 0.1%
0.04961206897 1
< 0.1%
0.05024509804 1
< 0.1%
ValueCountFrequency (%)
35.78740741 1
< 0.1%
35.28363636 1
< 0.1%
35.12611111 1
< 0.1%
34.79581818 1
< 0.1%
34.36407407 1
< 0.1%
33.7559322 1
< 0.1%
33.59107143 1
< 0.1%
33.45254237 1
< 0.1%
33.00084746 1
< 0.1%
32.71035714 1
< 0.1%

ROAS
Real number (ℝ)

HIGH CORRELATION 

Distinct99977
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.66115
Minimum0.024971847
Maximum1145.0743
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2024-12-08T23:30:08.873905image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.024971847
5-th percentile0.50670056
Q12.0753293
median5.0818523
Q312.756096
95-th percentile52.11139
Maximum1145.0743
Range1145.0494
Interquartile range (IQR)10.680767

Descriptive statistics

Standard deviation31.564643
Coefficient of variation (CV)2.3105407
Kurtosis172.80231
Mean13.66115
Median Absolute Deviation (MAD)3.743635
Skewness9.8289965
Sum1366115
Variance996.32668
MonotonicityNot monotonic
2024-12-08T23:30:09.109274image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.27862503 2
 
< 0.1%
5.215155152 2
 
< 0.1%
13.27424749 2
 
< 0.1%
3.079710145 2
 
< 0.1%
18.39308579 2
 
< 0.1%
12.84504792 2
 
< 0.1%
29 2
 
< 0.1%
15.61111111 2
 
< 0.1%
2.928571429 2
 
< 0.1%
5.666666667 2
 
< 0.1%
Other values (99967) 99980
> 99.9%
ValueCountFrequency (%)
0.02497184685 1
< 0.1%
0.02847441313 1
< 0.1%
0.03099730458 1
< 0.1%
0.03184142853 1
< 0.1%
0.0321845175 1
< 0.1%
0.03228743522 1
< 0.1%
0.03248444049 1
< 0.1%
0.03254488216 1
< 0.1%
0.03258224072 1
< 0.1%
0.03304440592 1
< 0.1%
ValueCountFrequency (%)
1145.074324 1
< 0.1%
1115.720588 1
< 0.1%
1061.974138 1
< 0.1%
1037.351351 1
< 0.1%
949.5136986 1
< 0.1%
894.056338 1
< 0.1%
830.9381443 1
< 0.1%
763.4064039 1
< 0.1%
756.0785714 1
< 0.1%
742.9907063 1
< 0.1%

Interactions

2024-12-08T23:29:58.276799image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:32.472332image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:34.723191image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:37.166538image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:39.472253image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:41.778467image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:44.138481image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:46.334863image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:48.784190image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:51.057798image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:53.363153image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:55.677939image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:58.458863image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:32.648950image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:34.894729image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:37.368857image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:39.640110image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:41.946048image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:44.312608image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:46.559264image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:48.965667image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:51.266244image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:53.592864image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:55.852958image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:58.667176image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:32.834865image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:35.073306image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:37.557187image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:39.814681image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:42.122561image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:44.490129image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:46.796146image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:49.156193image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:51.456820image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:53.781393image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:56.083015image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:58.846741image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:33.060353image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:35.259087image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:37.753069image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:39.989179image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:42.305038image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:44.675160image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:46.978695image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:49.351633image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:51.639956image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:53.971444image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:56.265672image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:59.018235image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:33.231857image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:35.461646image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:37.954982image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:40.174810image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:42.473974image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:44.843905image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:47.147243image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:49.531153image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:51.823533image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:54.159085image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:56.455691image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:59.189817image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:33.411377image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:35.634218image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:38.126223image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:40.379818image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:42.662497image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:45.029562image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:47.314793image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:49.706686image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:52.002106image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:54.346176image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:56.802358image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:59.358363image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:33.584065image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:35.811747image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:38.305693image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:40.548024image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:42.873446image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:45.213581image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:47.540156image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:49.896179image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:52.183622image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:54.523703image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:56.979266image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:59.525956image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:33.757209image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:35.982884image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:38.482860image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:40.716735image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:43.094853image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:45.392259image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:47.706303image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:50.080148image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:52.411013image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:54.709208image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:57.335502image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:59.732126image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:33.941382image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:36.399877image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:38.679285image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:40.904736image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:43.279704image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:45.580293image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:47.889339image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:50.265734image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:52.601552image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:54.913289image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:57.538721image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:59.929027image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:34.174045image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:36.613472image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:38.869410image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:41.089802image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:43.472222image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:45.766831image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:48.069375image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:50.468342image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:52.791314image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:55.108354image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:57.725386image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:30:00.126489image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:34.370513image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:36.806762image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:39.081361image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:41.278905image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:43.746495image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:45.952888image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:48.261006image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:50.676820image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:52.990114image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:55.300803image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:57.916747image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:30:00.311553image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:34.547657image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:36.988923image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:39.282201image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:41.482668image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:43.961765image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:46.132442image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:48.445552image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:50.866362image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:53.176686image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:55.490881image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-08T23:29:58.098259image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-12-08T23:30:09.289791image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
AOVAd_CPCAd_CTRAd_SpendCategoryClicksConversion_RateDiscount_AppliedImpressionsProduct_IDROASRegionRevenueUnits_Sold
AOV1.0000.0030.0060.0070.0530.0050.001-0.0970.005-0.0010.6110.0780.908-0.381
Ad_CPC0.0031.0000.0040.6790.004-0.005-0.0070.0030.006-0.003-0.4780.0000.002-0.003
Ad_CTR0.0060.0041.0000.6790.0030.0040.005-0.003-0.0050.004-0.4740.0000.0070.000
Ad_Spend0.0070.6790.6791.0000.003-0.001-0.001-0.0010.0010.001-0.7000.0060.006-0.003
Category0.0530.0040.0030.0031.0000.0000.0000.0000.0000.0930.0040.0000.0250.079
Clicks0.005-0.0050.004-0.0010.0001.0000.697-0.0030.002-0.0010.0060.0000.0070.004
Conversion_Rate0.001-0.0070.005-0.0010.0000.6971.000-0.002-0.635-0.0040.0030.0000.0020.001
Discount_Applied-0.0970.003-0.003-0.0010.000-0.003-0.0021.000-0.004-0.002-0.0730.000-0.110-0.000
Impressions0.0050.006-0.0050.0010.0000.002-0.635-0.0041.0000.0030.0030.0090.0050.001
Product_ID-0.001-0.0030.0040.0010.093-0.001-0.004-0.0020.0031.000-0.0040.000-0.001-0.002
ROAS0.611-0.478-0.474-0.7000.0040.0060.003-0.0730.003-0.0041.0000.0070.6690.010
Region0.0780.0000.0000.0060.0000.0000.0000.0000.0090.0000.0071.0000.0060.277
Revenue0.9080.0020.0070.0060.0250.0070.002-0.1100.005-0.0010.6690.0061.0000.010
Units_Sold-0.381-0.0030.000-0.0030.0790.0040.001-0.0000.001-0.0020.0100.2770.0101.000

Missing values

2024-12-08T23:30:00.557105image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-12-08T23:30:01.066293image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Product_IDTransaction_DateUnits_SoldDiscount_AppliedRevenueClicksImpressionsConversion_RateCategoryRegionAd_CTRAd_CPCAd_SpendAOVROAS
01402024-10-061340.14305.5411650.17210.01800.559.902.28014930.862626
15392024-10-291090.301102.19152010.07300.15890.4063.5610.11183517.340938
23062024-04-041160.04471.29161990.08400.05961.5089.404.0628455.271700
31362024-08-251250.20980.26123550.03110.04440.4419.547.84208050.166837
42882024-05-051320.07803.76443550.12020.12700.5367.316.08909111.941168
5382024-09-221600.2569.374730.05220.01531.1317.290.4335634.012146
65672023-12-081450.17121.56251100.23120.08891.97175.130.8383450.694113
75462024-04-301210.28208.6412080.00200.01331.2216.231.72429812.855206
87872024-04-101520.20757.3427730.37420.15621.55242.114.9825003.128082
9642024-11-241520.17163.74102990.03420.07280.7554.601.0772372.998901
Product_IDTransaction_DateUnits_SoldDiscount_AppliedRevenueClicksImpressionsConversion_RateCategoryRegionAd_CTRAd_CPCAd_SpendAOVROAS
999901632024-07-191630.25664.66464070.11200.02961.8454.464.07766912.204554
999918002024-11-051820.10777.02311960.16210.16070.67107.674.2693417.216681
999929842024-06-231480.08373.5284560.02010.06851.70116.452.5237843.207557
999935822024-01-211630.06213.2533400.01110.01350.456.071.30828235.131796
999942472024-01-071750.02142.1694610.02020.03631.1140.290.8123433.528419
999954152024-04-211790.11255.11414280.10210.10731.48158.801.4251961.606486
999967642024-09-171780.121302.6063630.02020.11640.3641.907.31797831.088305
999977182024-10-232120.26491.3654190.01220.12350.2024.702.31773619.893117
999983232024-01-031800.09598.9141123.42210.15660.96150.343.3272783.983704
999991392024-07-121570.20208.27343340.10400.09260.6156.491.3265613.686847